Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites
Open accessAbstract
Cryptic allosteric sites—transient pockets in a folded protein that are invisible to conventional experiments but can alter enzymatic activity via allosteric communication with the active site—are a promising opportunity for facilitating drug design by greatly expanding the repertoire of available drug targets. Unfortunately, identifying these sites is difficult, typically requiring resource-intensive screening of large libraries of small molecules. Here, we demonstrate that Markov state models built from extensive computer simulations (totaling hundreds of microseconds of dynamics) can identify prospective cryptic sites from the equilibrium fluctuations of three medically relevant proteins—β-lactamase, interleukin-2, and RNase H—even in the absence of any ligand. As in previous studies, our methods reveal a surprising variety of conformations—including bound-like configurations—that implies a role for conformational selection in ligand binding. Moreover, our analyses lead to a number of unique insights. First, direct comparison of simulations with and without the ligand reveals that there is still an important role for an induced fit during ligand binding to cryptic sites and suggests new conformations for docking. Second, correlations between amino acid sidechains can convey allosteric signals even in the absence of substantial backbone motions. Most importantly, our extensive sampling reveals a multitude of potential cryptic sites—consisting of transient pockets coupled to the active site—even in a single protein. Based on these observations, we propose that cryptic allosteric sites may be even more ubiquitous than previously thought and that our methods should be a valuable means of guiding the search for such sites.
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The intrinsic fluctuations of β-lactamase in its native state reveal a multitude of potential allosteric binding sites.
“The intrinsic fluctuations of β-lactamase in its native state reveals a multitude of potential allosteric binding sites”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [61]
MSMs can predict kinetic quantities on long timescales (e.g., milliseconds) using shorter MD simulations.
“MSMs can predict kinetic quantities on long timescales (e.g. milliseconds) using a set of much shorter MD simulations.395–396”
From Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell (2016) · cites this paper as [395]
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Density-based cluster algorithms for the identification of core sets
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Variational tensor approach for approximating the rare-event kinetics of macromolecular systems
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From byte to bench to bedside: molecular dynamics simulations and drug discovery
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Exploring Cryptic Pockets Formation in Targets of Pharmaceutical Interest with SWISH
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Structure based virtual screening: Fast and slow
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Folding@home: Achievements from over 20 years of citizen science herald the exascale era
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Self-citationAccurately modeling nanosecond protein dynamics requires at least microseconds of simulation
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Self-citationPrediction of New Stabilizing Mutations Based on Mechanistic Insights from Markov State Models
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Simulation of spontaneous G protein activation reveals a new intermediate driving GDP unbinding
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Self-citationCooperative Changes in Solvent Exposure Identify Cryptic Pockets, Switches, and Allosteric Coupling.
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Fluctuations within Folded Proteins: Implications for Thermodynamic and Allosteric Regulation
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Self-citationDetermination of Signaling Pathways in Proteins through Network Theory: Importance of the Topology
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Cloud computing approaches for prediction of ligand binding poses and pathways
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Using Markov state models to study self-assembly.
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A Genetically Encoded β‐Lactamase Reporter for Ultrasensitive 129Xe NMR in Mammalian Cells
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Quantitative comparison of alternative methods for coarse-graining biological networks
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Self-citationAdvanced Methods for Accessing Protein Shape-Shifting Present New Therapeutic Opportunities
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Enspara : Modeling molecular ensembles with scalable data structures and parallel computing
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Self-citationDefining NADH-Driven Allostery Regulating Apoptosis-Inducing Factor
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Self-citationA Universal Pattern in the Percolation and Dissipation of Protein Structural Perturbations
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Commute Maps: Separating Slowly Mixing Molecular Configurations for Kinetic Modeling
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Correlation-Based Feature Selection to Identify Functional Dynamics in Proteins
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Energy Propagation and Network Energetic Coupling in Proteins
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Evolutionarily Conserved Allosteric Communication in Protein Tyrosine Phosphatases
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FPocketWeb: protein pocket hunting in a web browser
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Structural Systems Pharmacology: The Role of 3D Structures in Next-Generation Drug Development
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Advances in methods for atomic resolution macromolecular structure determination
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Scanning Protein Surfaces with DNA‐Encoded Libraries
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Predicting Protein Dynamics and Allostery Using Multi-Protein Atomic Distance Constraints
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Druggability Assessment in TRAPP Using Machine Learning Approaches
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- Authors
- Bowman, G. R., & Geissler, P. L.
- Affiliations
- University of California, Berkeley (US)
- Lawrence Berkeley National Laboratory (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2012-07-02
- Volume
- 109
- Issue
- 29
- Pages
- 11681-11686
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3406870
- OpenAlex
- W2043580883
- Cited by
- 279
- References
- 61
- Citations by year
- 2026: 19 · 2025: 22 · 2024: 13 · 2023: 19 · 2022: 13 · 2021: 22 · 2020: 25 · 2019: 17 · 2018: 21 · 2017: 17 · 2016: 34 · 2015: 25 · 2014: 13 · 2013: 19
- FWCI
- 7.32
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM062868)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Keywords
- Markov state models, conformational selection, induced fit, ligand binding, beta-lactamase, interleukin-2, RNase H, allosteric communication, protein conformational sampling
- MeSH terms
- Allosteric Site, beta-Lactamases, Interleukin-2, Models, Chemical, Protein Binding, Ribonuclease H, Protein Folding, Molecular Dynamics Simulation
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